Summary of Credit Card Fraud Detection Using Advanced Transformer Model, by Chang Yu et al.
Credit Card Fraud Detection Using Advanced Transformer Model
by Chang Yu, Yongshun Xu, Jin Cao, Ye Zhang, Yinxin Jin, Mengran Zhu
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study leverages innovative applications of Transformer models for robust and precise credit card fraud detection. By processing data sources with meticulous care and balancing the dataset to address sparsity issues, the authors create a reliable foundation for their new Transformer model. A performance comparison is conducted with widely adopted models like SVM, Random Forest, Neural Network, and Logistic Regression, using metrics such as Precision, Recall, and F1 Score. The results demonstrate the Transformer model’s excellence in traditional applications and its potential in niche areas like fraud detection, offering a substantial advancement in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Credit card fraud is a growing concern for financial security. This study explores how to use special computer models called Transformers to stop this kind of fraud better. To make sure the data is reliable, the researchers worked hard to process and balance it. They then compared their new Transformer model with other popular models like Support Vector Machine, Random Forest, Neural Network, and Logistic Regression. The results show that the Transformer model works well in many situations and has great potential for stopping fraud. |
Keywords
» Artificial intelligence » F1 score » Logistic regression » Neural network » Precision » Random forest » Recall » Support vector machine » Transformer
